OpenRaiser/Intern-Atlas huggingface dataset:
Image Retrieval using Histogram Factorization and Contextual Similarity Learning
Venue: arXiv cs.CV 2013
Year: 2013

Image retrieval has been a top topic in the field of both computer vision and machine learning for a long time. Content based image retrieval, which tries to retrieve images from a database visually similar to a query image, has attracted much attention. Two most important issues of image retrieval are the representation and ranking of the images. Recently, bag-of-words based method has shown its power as a representation method. Moreover, nonnegative matrix factorization is also a popular way to represent the data samples. In addition, contextual similarity learning has also been studied and proven to be an effective method for the ranking problem. However, these technologies have never been used together. In this paper, we developed an effective image retrieval system by representing each image using the bag-of-words method as histograms, and then apply the nonnegative matrix factorization to factorize the histograms, and finally learn the ranking score using the contextual similarity learning method. The proposed novel system is evaluated on a large scale image database and the effectiveness is shown.
Google Scholar and the h-index in biomedicine: the popularization of bibliometric asessment
Venue: arXiv cs.DL 2013
Year: 2013

The aim of this paper is to review the features, benefits and limitations of the new scientific evaluation products derived from Google Scholar; Google Scholar Metrics and Google Scholar Citations, as well as the h-index which is the standard bibliometric indicator adopted by these services. It also outlines the potential of this new database as a source for studies in Biomedicine and compares the h-index obtained by the most relevant journals and researchers in the field of Intensive Care Medicine, by means of data extracted from Web of Science, Scopus and Google Scholar. Results show that, although average h-index values in Google Scholar are almost 30% higher than those obtained in Web of Science and about 15% higher than those collected by Scopus, there are no substantive changes in the rankings generated from either data source. Despite some technical problems, it is concluded that Google Scholar is a valid tool for researchers in Health Sciences, both for purposes of information retrieval and computation of bibliometric indicators
Web Services Modeling and Composition Approach using Object-Oriented Petri Nets
Venue: arXiv cs.SE 2013
Year: 2013

Nowadays, with the emergence and the evolution of new technologies, such as e-business, a large number of companies are connected to Internet, and have proposed web services to trade. Web services as presented, are conceptually limited components to relatively simple functionalities. Generally, a single service does not satisfy the users needs that are more and more complex. Therefore, services must be made able to be composed to offer added value services. In this paper, a web services composition approach, modelled by Objects-Oriented Petri nets, is presented. In his context, an expressive algebra, which successfully solves the web services complex composition problem, is proposed. A java tool that allows automating this approach; based on a definite algebra and a G-nets meta-model, proposed by us, is developed.
Automatic Fingerprint Recognition Using Minutiae Matching Technique for the Large Fingerprint Database
Venue: arXiv cs.CV 2013
Year: 2013

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification system. Because minutiae matching are certainly the most well-known and widely used method for fingerprint matching, minutiae are local discontinuities in the fingerprint pattern. In this paper a fingerprint matching algorithm is proposed using some specific feature of the minutiae points, also the acquired fingerprint image is considered by minimizing its size by generating a corresponding fingerprint template for a large fingerprint database. The results achieved are compared with those obtained through some other methods also shows some improvement in the minutiae detection process in terms of memory and time required.
An Improved GEF Fast Addition Algorithm
Venue: arXiv cs.DS 2013
Year: 2013

In this paper, an improved GEF fast addition algorithm is proposed. The proposed algorithm reduces time and memory space. In this algorithm, carry is calculated on the basis of arrival timing of the operand's bits without overhead of sorting. Intermediate terms are generated from the most significant bit and the carry is generated from the least significant bit using the functions of efficient operators. This algorithm shows better performance for use in the fastest computational devices of the near future.
The Deformed Consensus Protocol: Extended Version
Venue: arXiv cs.SY 2013
Year: 2013

This paper studies a generalization of the standard continuous-time consensus protocol, obtained by replacing the Laplacian matrix of the communication graph with the so-called deformed Laplacian. The deformed Laplacian is a second-degree matrix polynomial in the real variable 's' which reduces to the standard Laplacian for 's' equal to unity. The stability properties of the ensuing deformed consensus protocol are studied in terms of parameter 's' for some special families of undirected and directed graphs, and for arbitrary graph topologies by leveraging the spectral theory of quadratic eigenvalue problems. Examples and simulation results are provided to illustrate our theoretical findings.
Gossips and Prejudices: Ergodic Randomized Dynamics in Social Networks
Venue: arXiv cs.SY 2013
Year: 2013

In this paper we study a novel model of opinion dynamics in social networks, which has two main features. First, agents asynchronously interact in pairs, and these pairs are chosen according to a random process. We refer to this communication model as "gossiping". Second, agents are not completely open-minded, but instead take into account their initial opinions, which may be thought of as their "prejudices". In the literature, such agents are often called "stubborn". We show that the opinions of the agents fail to converge, but persistently undergo ergodic oscillations, which asymptotically concentrate around a mean distribution of opinions. This mean value is exactly the limit of the synchronous dynamics of the expected opinions.
Algorithms for Large-scale Whole Genome Association Analysis
Venue: arXiv cs.CE 2013
Year: 2013

In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for millions of polymorphisms. When handling these datasets, which exceed the main memory of contemporary computers, one faces two distinct challenges: 1) Millions of polymorphisms come at the cost of hundreds of Gigabytes of genotype data, which can only be kept in secondary storage; 2) the relatedness of the test population is represented by a covariance matrix, which, for large populations, can only fit in the combined main memory of a distributed architecture. In this paper, we present solutions for both challenges: The genotype data is streamed from and to secondary storage using a double buffering technique, while the covariance matrix is kept across the main memory of a distributed memory system. We show that these methods sustain high-performance and allow the analysis of enormous dataset
Embedding of Blink Frequency in Electrooculography Signal using Difference Expansion based Reversible Watermarking Technique
Venue: arXiv cs.CV 2013
Year: 2013

In the past few years, like other fields, rapid expansion of digitization and globalization has influenced the medical field as well. For progress of diagnostic results most of the reputed hospitals and diagnostic centres all over the world have started exchanging medical information. In this proposed method, the calculated diagnostic parametric values of the original Electrooculography (EOG) signal are embedded as a watermark by using Difference Expansion (DE) algorithm based reversible watermarking technique. The extracted watermark provides the required parametric values at the recipient end without any post computation of the recovered EOG signal. By computing the parametric values from the recovered signal, the integrity of the extracted watermark can be validated. The time domain features of EOG signal are calculated for the generation of watermark. In the current work, various features are studied and two major features related to blink frequency are used to generate the watermark. The high Signal to Noise Ratio (SNR) and the Bit Error Rate (BER) claim the robustness of the proposed method.
A primer on information theory, with applications to neuroscience
Venue: arXiv cs.IT 2013
Year: 2013

Given the constant rise in quantity and quality of data obtained from neural systems on many scales ranging from molecular to systems', information-theoretic analyses became increasingly necessary during the past few decades in the neurosciences. Such analyses can provide deep insights into the functionality of such systems, as well as a rigid mathematical theory and quantitative measures of information processing in both healthy and diseased states of neural systems. This chapter will present a short introduction to the fundamentals of information theory, especially suited for people having a less firm background in mathematics and probability theory. To begin, the fundamentals of probability theory such as the notion of probability, probability distributions, and random variables will be reviewed. Then, the concepts of information and entropy (in the sense of Shannon), mutual information, and transfer entropy (sometimes also referred to as conditional mutual information) will be outlined. As these quantities cannot be computed exactly from measured data in practice, estimation techniques for information-theoretic quantities will be presented. The chapter will conclude with the applications of information theory in the field of neuroscience, including questions of possible medical applications and a short review of software packages that can be used for information-theoretic analyses of neural data.
The structure of Bayes nets for vision recognition
Venue: arXiv cs.AI 2013
Year: 2013

This paper is part of a study whose goal is to show the effciency of using Bayes networks to carry out model based vision calculations. [Binford et al. 1987] Recognition proceeds by drawing up a network model from the object's geometric and functional description that predicts the appearance of an object. Then this network is used to find the object within a photographic image. Many existing and proposed techniques for vision recognition resemble the uncertainty calculations of a Bayes net. In contrast, though, they lack a derivation from first principles, and tend to rely on arbitrary parameters that we hope to avoid by a network model. The connectedness of the network depends on what independence considerations can be identified in the vision problem. Greater independence leads to easier calculations, at the expense of the net's expressiveness. Once this trade-off is made and the structure of the network is determined, it should be possible to tailor a solution technique for it. This paper explores the use of a network with multiply connected paths, drawing on both techniques of belief networks [Pearl 86] and influence diagrams. We then demonstrate how one formulation of a multiply connected network can be solved.
Probabilistic Causal Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

Predicting the future is an important component of decision making. In most situations, however, there is not enough information to make accurate predictions. In this paper, we develop a theory of causal reasoning for predictive inference under uncertainty. We emphasize a common type of prediction that involves reasoning about persistence: whether or not a proposition once made true remains true at some later time. We provide a decision procedure with a polynomial-time algorithm for determining the probability of the possible consequences of a set events and initial conditions. The integration of simple probability theory with temporal projection enables us to circumvent problems that nonmonotonic temporal reasoning schemes have in dealing with persistence. The ideas in this paper have been implemented in a prototype system that refines a database of causal rules in the course of applying those rules to construct and carry out plans in a manufacturing domain.
A Temporal Logic for Uncertain Events and An Outline of A Possible Implementation in An Extension of PROLOG
Venue: arXiv cs.AI 2013
Year: 2013

There is uncertainty associated with the occurrence of many events in real life. In this paper we develop a temporal logic to deal with such uncertain events and outline a possible implementation in an extension of PROLOG. Events are represented as fuzzy sets with the membership function giving the possibility of occurrence of the event in a given interval of time. The developed temporal logic is simple but powerful. It can determine effectively the various temporal relations between uncertain events or their combinations. PROLOG provides a uniform substrate on which to effectively implement such a temporal logic for uncertain events
Truth Maintenance Under Uncertainty
Venue: arXiv cs.AI 2013
Year: 2013

This paper addresses the problem of resolving errors under uncertainty in a rule-based system. A new approach has been developed that reformulates this problem as a neural-network learning problem. The strength and the fundamental limitations of this approach are explored and discussed. The main result is that neural heuristics can be applied to solve some but not all problems in rule-based systems.
Parallel Belief Revision
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes a formal system of belief revision developed by Wolfgang Spohn and shows that this system has a parallel implementation that can be derived from an influence diagram in a manner similar to that in which Bayesian networks are derived. The proof rests upon completeness results for an axiomatization of the notion of conditional independence, with the Spohn system being used as a semantics for the relation of conditional independence.
Probabilistic Inference and Probabilistic Reasoning
Venue: arXiv cs.AI 2013
Year: 2013

Uncertainty enters into human reasoning and inference in at least two ways. It is reasonable to suppose that there will be roles for these distinct uses of uncertainty also in automated reasoning.
Logical Fuzzy Optimization
Venue: arXiv cs.AI 2013
Year: 2013

We present a logical framework to represent and reason about fuzzy optimization problems based on fuzzy answer set optimization programming. This is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and maximum in the language of fuzzy answer set optimization programming to allow minimization or maximization of some desired criteria under fuzzy environments. We show the application of the proposed logical fuzzy optimization framework under the fuzzy answer set optimization programming to the fuzzy water allocation optimization problem.
Joint Iterative Power Adjustment and Interference Suppression Algorithms for Cooperative DS-CDMA Networks
Venue: arXiv cs.IT 2013
Year: 2013

This work presents joint iterative power allocation and interference suppression algorithms for DS-CDMA networks which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to individual and global power constraints and the design of linear receivers for interference suppression. We derive constrained minimum mean-squared error (MMSE) expressions for the parameter vectors that determine the optimal power levels across the relays and the parameters of the linear receivers. In order to solve the proposed optimization problems efficiently, we develop recursive least squares (RLS) algorithms for adaptive joint iterative power allocation, and receiver and channel parameter estimation. Simulation results show that the proposed algorithms obtain significant gains in performance and capacity over existing schemes.
RESLVE: Leveraging User Interest to Improve Entity Disambiguation on Short Text
Venue: arXiv cs.IR 2013
Year: 2013

We address the Named Entity Disambiguation (NED) problem for short, user-generated texts on the social Web. In such settings, the lack of linguistic features and sparse lexical context result in a high degree of ambiguity and sharp performance drops of nearly 50% in the accuracy of conventional NED systems. We handle these challenges by developing a model of user-interest with respect to a personal knowledge context; and Wikipedia, a particularly well-established and reliable knowledge base, is used to instantiate the procedure. We conduct systematic evaluations using individuals' posts from Twitter, YouTube, and Flickr and demonstrate that our novel technique is able to achieve substantial performance gains beyond state-of-the-art NED methods.
On the satisfiability problem for a 3-level quantified syllogistic
Venue: arXiv cs.LO 2013
Year: 2013

We show that a collection of three-sorted set-theoretic formulae, denoted TLQSR and which admits a restricted form of quantification over individual and set variables, has a solvable satisfiability problem by proving that it enjoys a small model property, i.e., any satisfiable TLQSR-formula psi has a finite model whose size depends solely on the size of psi itself. We also introduce the sublanguages (TLQSR)^h of TLQSR, whose formulae are characterized by having quantifier prefixes of length bounded by h \geq 2 and some other syntactic constraints, and we prove that each of them has the satisfiability problem NP-complete. Then, we show that the modal logic S5 can be formalized in (TLQSR)^3.
Design and Development of a Heart Rate Measuring Device using Fingertip
Venue: arXiv cs.OH 2013
Year: 2013

In this paper, we presented the design and development of a new integrated device for measuring heart rate using fingertip to improve estimating the heart rate. As heart related diseases are increasing day by day, the need for an accurate and affordable heart rate measuring device or heart monitor is essential to ensure quality of health. However, most heart rate measuring tools and environments are expensive and do not follow ergonomics. Our proposed Heart Rate Measuring (HRM) device is economical and user friendly and uses optical technology to detect the flow of blood through index finger. Three phases are used to detect pulses on the fingertip that include pulse detection, signal extraction, and pulse amplification. Qualitative and quantitative performance evaluation of the device on real signals shows accuracy in heart rate estimation, even under intense of physical activity. We compared the performance of HRM device with Electrocardiogram reports and manual pulse measurement of heartbeat of 90 human subjects of different ages. The results showed that the error rate of the device is negligible.
Containment of Nested Regular Expressions
Venue: arXiv cs.DB 2013
Year: 2013

Nested regular expressions (NREs) have been proposed as a powerful formalism for querying RDFS graphs, but research in a more general graph database context has been scarce, and static analysis results are currently lacking. In this paper we investigate the problem of containment of NREs, and show that it can be solved in PSPACE, i.e., the same complexity as the problem of containment of regular expressions or regular path queries (RPQs).
Is Shafer General Bayes?
Venue: arXiv cs.AI 2013
Year: 2013

This paper examines the relationship between Shafer's belief functions and convex sets of probability distributions. Kyburg's (1986) result showed that belief function models form a subset of the class of closed convex probability distributions. This paper emphasizes the importance of Kyburg's result by looking at simple examples involving Bernoulli trials. Furthermore, it is shown that many convex sets of probability distributions generate the same belief function in the sense that they support the same lower and upper values. This has implications for a decision theoretic extension. Dempster's rule of combination is also compared with Bayes' rule of conditioning.
Can Evidence Be Combined in the Dempster-Shafer Theory
Venue: arXiv cs.AI 2013
Year: 2013

Dempster's rule of combination has been the most controversial part of the Dempster-Shafer (D-S) theory. In particular, Zadeh has reached a conjecture on the noncombinability of evidence from a relational model of the D-S theory. In this paper, we will describe another relational model where D-S masses are represented as conditional granular distributions. By comparing it with Zadeh's relational model, we will show how Zadeh's conjecture on combinability does not affect the applicability of Dempster's rule in our model.
Using the Dempster-Shafer Scheme in a Diagnostic Expert System Shell
Venue: arXiv cs.AI 2013
Year: 2013

This paper discusses an expert system shell that integrates rule-based reasoning and the Dempster-Shafer evidence combination scheme. Domain knowledge is stored as rules with associated belief functions. The reasoning component uses a combination of forward and backward inferencing mechanisms to allow interaction with users in a mixed-initiative format.
Structuring Causal Tree Models with Continuous Variables
Venue: arXiv cs.AI 2013
Year: 2013

This paper considers the problem of invoking auxiliary, unobservable variables to facilitate the structuring of causal tree models for a given set of continuous variables. Paralleling the treatment of bi-valued variables in [Pearl 1986], we show that if a collection of coupled variables are governed by a joint normal distribution and a tree-structured representation exists, then both the topology and all internal relationships of the tree can be uncovered by observing pairwise dependencies among the observed variables (i.e., the leaves of the tree). Furthermore, the conditions for normally distributed variables are less restrictive than those governing bi-valued variables. The result extends the applications of causal tree models which were found useful in evidential reasoning tasks.
Implementing Evidential Reasoning in Expert Systems
Venue: arXiv cs.AI 2013
Year: 2013

The Dempster-Shafer theory has been extended recently for its application to expert systems. However, implementing the extended D-S reasoning model in rule-based systems greatly complicates the task of generating informative explanations. By implementing GERTIS, a prototype system for diagnosing rheumatoid arthritis, we show that two kinds of knowledge are essential for explanation generation: (l) taxonomic class relationships between hypotheses and (2) pointers to the rules that significantly contribute to belief in the hypothesis. As a result, the knowledge represented in GERTIS is richer and more complex than that of conventional rule-based systems. GERTIS not only demonstrates the feasibility of rule-based evidential-reasoning systems, but also suggests ways to generate better explanations, and to explicitly represent various useful relationships among hypotheses and rules.
A Heuristic Bayesian Approach to Knowledge Acquisition: Application to Analysis of Tissue-Type Plasminogen Activator
Venue: arXiv cs.AI 2013
Year: 2013

This paper describes a heuristic Bayesian method for computing probability distributions from experimental data, based upon the multivariate normal form of the influence diagram. An example illustrates its use in medical technology assessment. This approach facilitates the integration of results from different studies, and permits a medical expert to make proper assessments without considerable statistical training.